Computers and their capacities to process and store vast amounts of data present many interesting problems in efficient management of information. Data collections including millions of items can be stored. Such a catalog is useful in some implementations when it can be efficiently searched for items of interest.
Products, services, subscriptions, and other offerings are available via online sites. Online sites can provide recommendations of similar or related products based on what other customers have purchased or viewed. Nonetheless, these generic recommendations may not be effective to categorize a visitor and/or persuade the visitor to the online site to make a purchase.
Visitor activity may be used to generate predictions about a visitor. Such predictions may categorize and/or persuade a visitor. However, the amount of activity for a given visitor may be highly granular (e.g., many interaction data points such as mouse movements, purchases, page views, etc.) and collected over a long period of time (e.g., days, months, or years). Hence, there is a need for improved systems and methods of leveraging visitor activity for generating predictions for visitors.